def test_edmonds3_minbranch2(): G = G1() G.add_edge(8, 9, weight=-10) x = branchings.minimum_branching(G) edges = [(8, 9, -10)] x_ = build_branching(edges) assert_equal_branchings(x, x_)
def test_edmonds3_minbranch2(): G = G1() G.add_edge(8, 9, weight=-10) x = branchings.minimum_branching(G) edges = [(8, 9, -10)] x_ = build_branching(edges) assert_equal_branchings(x, x_)
def test_edmonds1_minbranch(): # Using -G_array and min should give the same as optimal_arborescence_1, # but with all edges negative. edges = [(u, v, -w) for (u, v, w) in optimal_arborescence_1] G = nx.from_numpy_array(-G_array, create_using=nx.DiGraph) # Quickly make sure max branching is empty. x = branchings.maximum_branching(G) x_ = build_branching([]) assert_equal_branchings(x, x_) # Now test the min branching. x = branchings.minimum_branching(G) x_ = build_branching(edges) assert_equal_branchings(x, x_)
def test_edmonds1_minbranch(): # Using -G_array and min should give the same as optimal_arborescence_1, # but with all edges negative. edges = [ (u, v, -w) for (u, v, w) in optimal_arborescence_1 ] G = nx.DiGraph() G = nx.from_numpy_matrix(-G_array, create_using=G) # Quickly make sure max branching is empty. x = branchings.maximum_branching(G) x_ = build_branching([]) assert_equal_branchings(x, x_) # Now test the min branching. x = branchings.minimum_branching(G) x_ = build_branching(edges) assert_equal_branchings(x, x_)
def test_edmonds3_minbranch1(): G = G1() x = branchings.minimum_branching(G) edges = [] x_ = build_branching(edges) assert_equal_branchings(x, x_)
def test_edmonds3_minbranch1(): G = G1() x = branchings.minimum_branching(G) edges = [] x_ = build_branching(edges) assert_equal_branchings(x, x_)